{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignore numpy dtype warnings. These warnings are caused by an interaction\n",
    "# between numpy and Cython and can be safely ignored.\n",
    "# Reference: https://stackoverflow.com/a/40846742\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import nbinteract as nbi\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "np.set_printoptions(threshold=20, precision=2, suppress=True)\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "pd.set_option('precision', 2)\n",
    "# This option stops scientific notation for pandas\n",
    "# pd.set_option('display.float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def df_interact(df, nrows=7, ncols=7):\n",
    "    '''\n",
    "    Outputs sliders that show rows and columns of df\n",
    "    '''\n",
    "    def peek(row=0, col=0):\n",
    "        return df.iloc[row:row + nrows, col:col + ncols]\n",
    "    if len(df.columns) <= ncols:\n",
    "        interact(peek, row=(0, len(df) - nrows, nrows), col=fixed(0))\n",
    "    else:\n",
    "        interact(peek,\n",
    "                 row=(0, len(df) - nrows, nrows),\n",
    "                 col=(0, len(df.columns) - ncols))\n",
    "    print('({} rows, {} columns) total'.format(df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('water_large.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "Curve = namedtuple('Curve', ['xs', 'ys'])\n",
    "\n",
    "def flatten(seq): return [item for subseq in seq for item in subseq]\n",
    "\n",
    "def make_curve(clf, x_start=-50, x_end=50):\n",
    "    xs = np.linspace(x_start, x_end, num=100)\n",
    "    ys = clf.predict(xs.reshape(-1, 1))\n",
    "    return Curve(xs, ys)\n",
    "\n",
    "def plot_data(df=df, ax=plt, **kwargs):\n",
    "    ax.scatter(df.iloc[:, 0], df.iloc[:, 1], s=50, **kwargs)\n",
    "\n",
    "def plot_curve(curve, ax=plt, **kwargs):\n",
    "    ax.plot(curve.xs, curve.ys, **kwargs)\n",
    "    \n",
    "def plot_curves(curves, cols=2, labels=None):\n",
    "    if labels is None:\n",
    "        labels = [f'Deg {deg} poly' for deg in degrees]\n",
    "    rows = int(np.ceil(len(curves) / cols))\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(10, 8),\n",
    "                             sharex=True, sharey=True)\n",
    "    for ax, curve, label in zip(flatten(axes), curves, labels):\n",
    "        plot_data(ax=ax, label='Training data')\n",
    "        plot_curve(curve, ax=ax, label=label)\n",
    "        ax.set_ylim(-5e10, 170e10)\n",
    "        ax.legend()\n",
    "        \n",
    "    # add a big axes, hide frame\n",
    "    fig.add_subplot(111, frameon=False)\n",
    "    # hide tick and tick label of the big axes\n",
    "    plt.tick_params(labelcolor='none', top='off', bottom='off',\n",
    "                    left='off', right='off')\n",
    "    plt.grid(False)\n",
    "    plt.title('Polynomial Regression')\n",
    "    plt.xlabel('Water Level Change (m)')\n",
    "    plt.ylabel('Water Flow (Liters)')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def coefs(clf):\n",
    "    reg = clf.named_steps['reg']\n",
    "    return np.append(reg.intercept_, reg.coef_)\n",
    "\n",
    "def coef_table(clf):\n",
    "    vals = coefs(clf)\n",
    "    return (pd.DataFrame({'Coefficient Value': vals})\n",
    "            .rename_axis('degree'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:, [0]].as_matrix()\n",
    "y = df.iloc[:, 1].as_matrix()\n",
    "\n",
    "degrees = [1, 2, 8, 12]\n",
    "clfs = [Pipeline([('poly', PolynomialFeatures(degree=deg, include_bias=False)),\n",
    "                  ('reg', LinearRegression())])\n",
    "        .fit(X, y)\n",
    "        for deg in degrees]\n",
    "\n",
    "curves = [make_curve(clf) for clf in clfs]\n",
    "\n",
    "alphas = [0.01, 0.1, 1.0, 10.0]\n",
    "\n",
    "ridge_clfs = [Pipeline([('poly', PolynomialFeatures(degree=deg, include_bias=False)),\n",
    "                        ('reg', RidgeCV(alphas=alphas, normalize=True))])\n",
    "        .fit(X, y)\n",
    "        for deg in degrees]\n",
    "\n",
    "ridge_curves = [make_curve(clf) for clf in ridge_clfs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L2 Regularization: Ridge Regression\n",
    "\n",
    "In this section we introduce $ L_2 $ regularization, a method of penalizing large weights in our cost function to lower model variance. We briefly review linear regression, then introduce regularization as a modification to the cost function.\n",
    "\n",
    "To perform least squares linear regression, we use the model:\n",
    "\n",
    "$$\n",
    "f_\\hat{\\theta}(x) = \\hat{\\theta} \\cdot x\n",
    "$$\n",
    "\n",
    "We fit the model by minimizing the mean squared error cost function:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "L(\\hat{\\theta}, X, y)\n",
    "&= \\frac{1}{n} \\sum_{i}^n(y_i - f_\\hat{\\theta} (X_i))^2\\\\\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "In the above definitions, $ X $ represents the $ n \\times p $ data matrix, $ x $ represents a row of $ X $, $ y $ represents the observed outcomes, and $ \\hat{\\theta} $ represents the model weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L2 Regularization Definition\n",
    "\n",
    "To add $ L_2 $ regularization to the model, we modify the cost function above:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "L(\\hat{\\theta}, X, y)\n",
    "&= \\frac{1}{n} \\sum_{i}(y_i - f_\\hat{\\theta} (X_i))^2\n",
    "    + \\lambda \\sum_{j = 1}^{p} \\hat{\\theta_j}^2\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Notice that the cost function above is the same as before with the addition of the $ L_2 $ regularization $ \\lambda \\sum_{j = 1}^{p} \\hat{\\theta_j}^2 $ term. The summation in this term sums the square of each model weight $ \\hat{\\theta_1}, \\hat{\\theta_2}, \\ldots, \\hat{\\theta_p} $. The term also introduces a new scalar model parameter $ \\lambda $ that adjusts the regularization penalty.\n",
    "\n",
    "The regularization term causes the cost to increase if the values in $ \\hat{\\theta} $ are further away from 0. With the addition of regularization, the optimal model weights minimize the combination of loss and regularization penalty rather than the loss alone. Since the resulting model weights tend to be smaller in absolute value, the model has lower variance and higher bias.\n",
    "\n",
    "Using $ L_2 $ regularization with a linear model and the mean squared error cost function is also known more commonly as **ridge regression**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Regularization Parameter\n",
    "\n",
    "The regularization parameter $ \\lambda $ controls the regularization penalty. A small $ \\lambda $ results in a small penalty—if $ \\lambda = 0 $ the regularization term is also $ 0 $ and the cost is not regularized at all.\n",
    "\n",
    "A large $ \\lambda $ terms results in a large penalty and therefore a simpler model. Increasing $ \\lambda $ decreases the variance and increases the bias of the model. We use cross-validation to select the value of $ \\lambda $ that minimizes the validation error.\n",
    "\n",
    "**Note about regularization in `scikit-learn`:**\n",
    "\n",
    "`scikit-learn` provides regression models that have regularization built-in. For example, to conduct ridge regression you may use the [`sklearn.linear_model.Ridge`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) regression model. Note that `scikit-learn` models call the regularization parameter `alpha` instead of $ \\lambda $.\n",
    "\n",
    "`scikit-learn` conveniently provides regularized models that perform cross-validation to select a good value of $ \\lambda $. For example, the [`sklearn.linear_model.RidgeCV`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV) allows users to input regularization parameter values and will automatically use cross-validation to select the parameter value with the least validation error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias Term Exclusion\n",
    "\n",
    "Note that the bias term $ \\theta_0 $ is not included in the summation of the regularization term. We do not penalize the bias term because increasing the bias term does not increase the variance of our model—the bias term simply shifts all predictions by a constant value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Normalization\n",
    "\n",
    "Notice that the regularization term $ \\lambda \\sum_{j = 1}^{p} \\hat{\\theta_j}^2 $ penalizes each $ \\hat{\\theta_j} $ equally. However, the effect of each $ \\hat{\\theta_j} $ differs depending on the data itself. Consider this section of the water flow dataset after adding degree 8 polynomial features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deg_0_feat</th>\n",
       "      <th>deg_1_feat</th>\n",
       "      <th>...</th>\n",
       "      <th>deg_6_feat</th>\n",
       "      <th>deg_7_feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-15.94</td>\n",
       "      <td>253.98</td>\n",
       "      <td>...</td>\n",
       "      <td>-261095791.08</td>\n",
       "      <td>4161020472.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-29.15</td>\n",
       "      <td>849.90</td>\n",
       "      <td>...</td>\n",
       "      <td>-17897014961.65</td>\n",
       "      <td>521751305227.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.19</td>\n",
       "      <td>1309.68</td>\n",
       "      <td>...</td>\n",
       "      <td>81298431147.09</td>\n",
       "      <td>2942153527269.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.49</td>\n",
       "      <td>1405.66</td>\n",
       "      <td>...</td>\n",
       "      <td>104132296999.30</td>\n",
       "      <td>3904147586408.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-48.06</td>\n",
       "      <td>2309.65</td>\n",
       "      <td>...</td>\n",
       "      <td>-592123531634.12</td>\n",
       "      <td>28456763821657.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   deg_0_feat  deg_1_feat        ...              deg_6_feat        deg_7_feat\n",
       "0      -15.94      253.98        ...           -261095791.08     4161020472.12\n",
       "1      -29.15      849.90        ...         -17897014961.65   521751305227.70\n",
       "2       36.19     1309.68        ...          81298431147.09  2942153527269.12\n",
       "3       37.49     1405.66        ...         104132296999.30  3904147586408.71\n",
       "4      -48.06     2309.65        ...        -592123531634.12 28456763821657.78\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(clfs[2].named_steps['poly'].transform(X[:5]),\n",
    "             columns=[f'deg_{n}_feat' for n in range(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the degree 7 polynomial features have much larger values than the degree 1 features. This means that a large model weight for the degree 7 features affects the predictions much more than a large model weight for the degree 1 features. If we apply regularization to this data directly, the regularization penalty will disproportionately lower the model weight for the lower degree features. In practice, this often results in high model variance even after applying regularization since the features with large effect on prediction will not be affected.\n",
    "\n",
    "To combat this, we *normalize* each data column by subtracting the mean and scaling the values in each column to be between -1 and 1. In `scikit-learn`, most regression models allow initializing with `normalize=True` to normalize the data before fitting.\n",
    "\n",
    "Another analogous technique is *standardizing* the data columns by subtracting the mean and dividing by the standard deviation for each data column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Ridge Regression\n",
    "\n",
    "We have previously used polynomial features to fit polynomials of degree 2, 8, and 12 to water flow data. The original data and resulting model predictions are repeated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>water_level_change</th>\n",
       "      <th>water_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-15.94</td>\n",
       "      <td>60422330445.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-29.15</td>\n",
       "      <td>33214896575.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.19</td>\n",
       "      <td>972706380901.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.09</td>\n",
       "      <td>236352046523.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>46.28</td>\n",
       "      <td>1494256381086.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.61</td>\n",
       "      <td>378146284247.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    water_level_change       water_flow\n",
       "0               -15.94   60422330445.52\n",
       "1               -29.15   33214896575.60\n",
       "2                36.19  972706380901.06\n",
       "..                 ...              ...\n",
       "20                7.09  236352046523.78\n",
       "21               46.28 1494256381086.73\n",
       "22               14.61  378146284247.97\n",
       "\n",
       "[23 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d815e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(curves)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conduct ridge regression, we first extract the data matrix and the vector of outcomes from the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: \n",
      "[[-15.94]\n",
      " [-29.15]\n",
      " [ 36.19]\n",
      " ...\n",
      " [  7.09]\n",
      " [ 46.28]\n",
      " [ 14.61]]\n",
      "\n",
      "y: \n",
      "[6.04e+10 3.32e+10 9.73e+11 ... 2.36e+11 1.49e+12 3.78e+11]\n"
     ]
    }
   ],
   "source": [
    "X = df.iloc[:, [0]].as_matrix()\n",
    "y = df.iloc[:, 1].as_matrix()\n",
    "print('X: ')\n",
    "print(X)\n",
    "print()\n",
    "print('y: ')\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we apply a degree 12 polynomial transform to `X`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First two rows of transformed X:\n",
      "[[-1.59e+01  2.54e+02 -4.05e+03  6.45e+04 -1.03e+06  1.64e+07 -2.61e+08\n",
      "   4.16e+09]\n",
      " [-2.92e+01  8.50e+02 -2.48e+04  7.22e+05 -2.11e+07  6.14e+08 -1.79e+10\n",
      "   5.22e+11]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# We need to specify include_bias=False since sklearn's classifiers\n",
    "# automatically add the intercept term.\n",
    "X_poly_8 = PolynomialFeatures(degree=8, include_bias=False).fit_transform(X)\n",
    "print('First two rows of transformed X:')\n",
    "print(X_poly_8[0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify `alpha` values that `scikit-learn` will select from using cross-validation, then use the `RidgeCV` classifier to fit the transformed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1.0, 10.0]\n",
    "\n",
    "# Remember to set normalize=True to normalize data\n",
    "clf = RidgeCV(alphas=alphas, normalize=True).fit(X_poly_8, y)\n",
    "\n",
    "# Display the chosen alpha value:\n",
    "clf.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the model predictions for the base degree 8 polynomial classifier next to the regularized degree 8 classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c8cc2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_data()\n",
    "plot_curve(curves[2])\n",
    "plt.title('Base degree 8 polynomial')\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_data()\n",
    "plot_curve(ridge_curves[2])\n",
    "plt.title('Regularized degree 8 polynomial')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the regularized polynomial is smoother than the base degree 8 polynomial and still captures the major trend in the data.\n",
    "\n",
    "Comparing the coefficients of the non-regularized and regularized models shows that ridge regression favors placing model weights on the lower degree polynomial terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Base</th>\n",
       "      <th>Regularized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>225782472111.94</td>\n",
       "      <td>221063525725.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13115217770.78</td>\n",
       "      <td>6846139065.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-144725749.98</td>\n",
       "      <td>146158037.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-10355082.91</td>\n",
       "      <td>1930090.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>567935.23</td>\n",
       "      <td>38240.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9805.14</td>\n",
       "      <td>564.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-249.64</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.09</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Base     Regularized\n",
       "degree                                \n",
       "0      225782472111.94 221063525725.23\n",
       "1       13115217770.78   6846139065.96\n",
       "2        -144725749.98    146158037.96\n",
       "3         -10355082.91      1930090.04\n",
       "4            567935.23        38240.62\n",
       "5              9805.14          564.21\n",
       "6              -249.64            7.25\n",
       "7                -2.09            0.18\n",
       "8                 0.03            0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = coef_table(clfs[2]).rename(columns={'Coefficient Value': 'Base'})\n",
    "ridge = coef_table(ridge_clfs[2]).rename(columns={'Coefficient Value': 'Regularized'})\n",
    "\n",
    "pd.options.display.max_rows = 20\n",
    "display(base.join(ridge))\n",
    "pd.options.display.max_rows = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeating the process for degree 12 polynomial features results in a similar result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c8a9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_data()\n",
    "plot_curve(curves[3])\n",
    "plt.title('Base degree 12 polynomial')\n",
    "plt.ylim(-5e10, 170e10)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_data()\n",
    "plot_curve(ridge_curves[3])\n",
    "plt.title('Regularized degree 12 polynomial')\n",
    "plt.ylim(-5e10, 170e10)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing the regularization parameter results in progressively simpler models. The plot below demonstrates the effects of increasing the regularization amount from 0.001 to 100.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e4ddc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]\n",
    "\n",
    "alpha_clfs = [Pipeline([\n",
    "    ('poly', PolynomialFeatures(degree=12, include_bias=False)),\n",
    "    ('reg', Ridge(alpha=alpha, normalize=True))]\n",
    ").fit(X, y) for alpha in alphas]\n",
    "\n",
    "alpha_curves = [make_curve(clf) for clf in alpha_clfs]\n",
    "labels = [f'$\\\\lambda = {alpha}$' for alpha in alphas]\n",
    "\n",
    "plot_curves(alpha_curves, cols=3, labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, increasing the regularization parameter increases the bias of our model. If our parameter is too large, the model becomes a constant model because any non-zero model weight is heavily penalized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Using $ L_2 $ regularization allows us to tune model bias and variance by penalizing large model weights. $ L_2 $ regularization for least squares linear regression is also known by the more common name ridge regression. Using regularization adds an additional model parameter $ \\lambda $ that we adjust using cross-validation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
